import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import xarray as xr
from matplotlib import pyplot as plt
da = xr.open_dataset("NOAA_NCDC_ERSST_v3b_SST.nc",engine="netcdf4")
weights = np.cos(np.deg2rad(da.lat))
sst_weighted = da.sst.sel(lat=slice(-5,5),lon=slice(210,240)).weighted(weights)
region_mean = sst_weighted.mean(dim=('lon','lat'))
monthly_sst = region_mean.groupby('time.month').mean()
monthly_sst
<xarray.DataArray 'sst' (month: 12)>
array([26.024014, 26.362394, 27.003092, 27.491852, 27.456303, 27.141228,
26.58982 , 26.112291, 25.98521 , 25.974686, 25.978508, 25.953588],
dtype=float32)
Coordinates:
* month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12array([26.024014, 26.362394, 27.003092, 27.491852, 27.456303, 27.141228,
26.58982 , 26.112291, 25.98521 , 25.974686, 25.978508, 25.953588],
dtype=float32)array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=int64)
x = region_mean.size
anoma = np.zeros(x-2)
for i in range(0,x-2):
k = i % 12
anoma[i] = region_mean[i] - monthly_sst[k]
print(anoma)
[-3.30963135e-01 -4.54664230e-01 -2.80950546e-01 -2.05434799e-01 -2.30035782e-01 -3.66180420e-01 -6.16817474e-02 1.39263153e-01 -1.82481766e-01 -5.41425705e-01 -5.23983002e-01 -3.64625931e-01 -3.14182281e-01 -2.28069305e-01 -2.70000458e-01 -7.27558136e-02 1.59219742e-01 2.20666885e-01 -1.81058884e-01 -5.52108765e-01 -1.00966263e+00 -9.02921677e-01 -6.00603104e-01 -4.50906754e-01 -3.74057770e-01 -3.43709946e-01 -4.38940048e-01 -5.22491455e-01 -6.29348755e-01 -4.49424744e-01 -2.49549866e-01 -2.83357620e-01 -7.10056305e-01 -6.79294586e-01 -8.98977280e-01 -9.90926743e-01 -7.22728729e-01 -3.17501068e-01 1.08222961e-01 3.90274048e-01 8.86363983e-02 3.47993851e-01 8.60031128e-01 8.39574814e-01 6.82573318e-01 8.26736450e-01 9.65261459e-01 1.19773102e+00 8.20079803e-01 4.90779877e-01 -1.26127243e-01 -8.39115143e-01 -1.07551765e+00 -1.11114883e+00 -6.98534012e-01 -1.13474846e+00 -1.10546494e+00 -1.05469704e+00 -1.17579079e+00 -1.19849396e+00 -7.82169342e-01 -3.96112442e-01 -2.02445984e-02 4.69264984e-02 3.72009277e-01 6.20592117e-01 8.77420425e-01 1.22791100e+00 1.32556534e+00 1.47460747e+00 1.67679214e+00 1.58392525e+00 1.18647575e+00 8.05919647e-01 7.27197647e-01 4.18764114e-01 -4.47118759e-01 -8.49971771e-02 -1.14250183e-02 -4.26727295e-01 -5.45509338e-01 -4.80216980e-01 -5.26971817e-01 -6.34326935e-01 -4.05538559e-01 -2.69256592e-01 -6.02115631e-01 -1.02444839e+00 -3.98242950e-01 1.77974701e-02 -1.72922134e-01 -4.08275604e-01 -8.08877945e-01 -7.55228043e-01 -6.49543762e-01 -7.67507553e-01 -1.01075745e+00 -1.18670082e+00 -1.04150772e+00 -7.02096939e-01 -6.85894012e-01 -2.74696350e-02 4.52142715e-01 2.59229660e-01 5.73654175e-02 2.30892181e-01 5.01739502e-01 6.80013657e-01 8.52725983e-01 9.68412399e-01 5.75172424e-01 5.08401871e-01 6.01848602e-01 3.18761826e-01 -1.73568726e-03 2.45704651e-01 5.52688599e-01 6.53747559e-01 5.33721924e-01 7.29692459e-01 5.26790619e-01 1.70495987e-01 1.81064606e-02 2.34432220e-02 -3.21191788e-01 -8.53246689e-01 -1.08705330e+00 -1.15822029e+00 -9.06755447e-01 -1.08447266e+00 -1.05813026e+00 -1.21119308e+00 -1.54037666e+00 -1.44580650e+00 -1.17505455e+00 -9.03314590e-01 -7.57276535e-01 -9.16641235e-01 -8.74408722e-01 -9.15594101e-01 -7.88028717e-01 -1.13399315e+00 -1.11263084e+00 -1.28592300e+00 -8.63222122e-01 -4.85475540e-01 -9.25598145e-02 2.33209610e-01 4.35466766e-01 6.56845093e-01 1.14994049e+00 1.51198196e+00 1.49617195e+00 1.86521721e+00 2.17369080e+00 2.41566086e+00 2.04671478e+00 1.22564125e+00 4.15952682e-01 -4.59506989e-01 -9.85071182e-01 -9.98487473e-01 -1.40979385e+00 -1.40398598e+00 -1.43551826e+00 -1.70537186e+00 -2.22385597e+00 -2.13230896e+00 -1.97341156e+00 -1.55602837e+00 -1.17746544e+00 -8.21626663e-01 -8.26705933e-01 -7.67297745e-01 -5.58120728e-01 -3.48073959e-01 -4.64656830e-01 -7.81883240e-01 -1.06403923e+00 -8.48754883e-01 -3.02171707e-01 -6.06157303e-01 -8.15513611e-01 -5.72696686e-01 -8.02507401e-01 -1.28851318e+00 -1.09246254e+00 -1.10377312e+00 -1.29801941e+00 -1.62760544e+00 -1.30681038e+00 -1.79863358e+00 -1.88496017e+00 -1.14078712e+00 -6.24166489e-01 -6.89245224e-01 -4.10305023e-01 -2.20394135e-02 2.56885529e-01 5.04529953e-01 6.29997253e-01 9.35348511e-01 8.68396759e-01 7.75320053e-01 9.83037949e-01 6.97431564e-01 4.47166443e-01 -2.34815598e-01 1.58781052e-01 3.04380417e-01 1.99649811e-01 -6.58416748e-02 3.94937515e-01 6.73301697e-01 5.98134995e-01 6.48046494e-01 6.48948669e-01 3.75152588e-01 -4.02431488e-02 -5.61210632e-01 -6.60833359e-01 -5.12590408e-01 -3.82158279e-01 -5.96029282e-01 -5.56280136e-01 -1.66837692e-01 -1.40781403e-01 4.91142273e-03 -2.34390259e-01 -1.72597885e-01 3.15475464e-01 3.02062988e-01 7.25917816e-02 -1.04923248e-02 -2.50164032e-01 1.03847504e-01 6.15066528e-01 4.35447693e-01 4.85567093e-01 5.51925659e-01 5.96817017e-01 2.94038773e-01 7.90004730e-02 -5.22994995e-03 9.21134949e-02 3.71648788e-01 9.26990509e-02 -2.12642670e-01 -1.62235260e-01 -2.97018051e-01 1.83677673e-03 1.06241226e-01 -3.52642059e-01 -6.17816925e-01 -4.60607529e-01 -3.21254730e-01 -3.27144623e-01 -1.61096573e-01 -4.61927414e-01 -5.86065292e-01 -1.13357544e-01 -1.06512070e-01 -2.61051178e-01 -1.08089447e-01 2.29475021e-01 1.15242004e-01 1.17872238e-01 1.96640015e-01 7.06979752e-01 8.37705612e-01 5.26569366e-01 9.94503021e-01 1.79734612e+00 2.28138161e+00 2.42700195e+00 2.83947372e+00 2.91114044e+00 2.34492111e+00 1.75108910e+00 1.32913780e+00 1.31182098e+00 8.95385742e-01 2.70500183e-02 -1.74716949e-01 -4.39016342e-01 -9.95325089e-01 -1.21386147e+00 -8.02986145e-01 -3.19398880e-01 2.37405777e-01 7.32822418e-02 -2.20182419e-01 -5.24604797e-01 -8.30909729e-01 -4.89503860e-01 -1.11373901e-01 -2.38319397e-01 -5.72021484e-01 -1.08501625e+00 -1.24806786e+00 -1.19357109e+00 -6.05335236e-01 -6.35246277e-01 -7.14372635e-01 -6.42564774e-01 -6.83153152e-01 -6.44767761e-01 -6.63862228e-01 -7.35221863e-01 -5.49530029e-01 -4.01174545e-01 -5.32310486e-01 -6.23764038e-01 -4.49476242e-01 -1.47380829e-01 6.03370667e-02 -5.95302582e-02 2.33345032e-02 3.03169250e-01 4.72887039e-01 6.23832703e-01 8.93800735e-01 1.02175522e+00 1.22146225e+00 1.30290985e+00 1.38529396e+00 1.32454491e+00 1.13621330e+00 9.87726212e-01 1.06029320e+00 1.32797432e+00 1.69221306e+00 1.77561378e+00 1.26540184e+00 1.24256897e+00 1.04600143e+00 9.39210892e-01 1.84955597e-01 1.77923203e-01 -1.69374466e-01 -9.96715546e-01 -1.70558929e+00 -1.82403374e+00 -1.27101135e+00 -1.02985764e+00 -1.75619125e+00 -1.91534424e+00 -1.95112801e+00 -1.81334496e+00 -1.22181511e+00 -1.02056885e+00 -7.60812759e-01 -5.05804062e-01 -8.32557678e-02 -2.95034409e-01 -3.64562988e-01 -2.32288361e-01 -3.42851639e-01 -3.97029877e-01 -1.03151321e-01 -5.41038513e-02 1.71092987e-01 -2.44579315e-02 2.26760864e-01 4.05338287e-01 7.17334747e-02 2.70549774e-01 3.50460052e-01 3.16377640e-01 2.09562302e-01 7.55043030e-02 1.96365356e-01 4.26448822e-01 3.24892044e-01 7.60478973e-02 3.65451813e-01 5.78996658e-01 9.59886551e-01 1.11251450e+00 8.54352951e-01 5.41435242e-01 8.24377060e-01 1.24568176e+00 1.63202095e+00 1.85447693e+00 1.72328758e+00 1.41114616e+00 1.47615433e+00 1.38861084e+00 8.21783066e-01 3.07975769e-01 1.07847214e-01 -7.77606964e-02 -3.56052399e-01 -2.08093643e-01 -8.06407928e-02 2.45565414e-01 5.71861267e-01 5.26227951e-01 1.00234413e+00 1.13142204e+00 4.76942062e-01 4.32010651e-01 2.83992767e-01 3.75091553e-01 2.86462784e-01 1.56719208e-01 7.67192841e-02 1.55960083e-01 1.88282013e-01 1.48155212e-01 2.40964890e-01 3.73693466e-01 4.32407379e-01 2.72422791e-01 2.86005020e-01 2.83836365e-01 6.85518265e-01 1.21235275e+00 1.21360970e+00 1.00916672e+00 7.54686356e-01 4.03032303e-01 1.82216644e-01 -2.39391327e-01 2.19707489e-02 -8.00514221e-03 -6.58277512e-01 -8.10800552e-01 -8.28147888e-01 -1.00267029e+00 -7.34460831e-01 -6.39331818e-01 -6.38004303e-01 -4.84519958e-01 -2.89472580e-01 -1.51567459e-01 -1.36272430e-01 -2.16087341e-01 -1.12840652e-01 -3.65085602e-01 -3.01784515e-01 -3.25988770e-01 -6.78546906e-01 -7.29331970e-01 -5.53371429e-01 -2.90473938e-01 2.87551880e-02 7.96968460e-01 1.41324615e+00 1.88284302e+00 2.16668892e+00 2.43552017e+00 2.78508377e+00 2.88759804e+00 2.86781883e+00 2.80737877e+00 2.26365089e+00 1.69120789e+00 1.25765991e+00 9.13053513e-01 -8.35552216e-02 -7.38901138e-01 -9.20829773e-01 -1.18492889e+00 -1.28274727e+00 -1.08488274e+00 -1.44519424e+00 -1.62586212e+00 -1.08290482e+00 -6.28887177e-01 -6.49038315e-01 -7.72077560e-01 -8.52817535e-01 -8.57444763e-01 -9.65232849e-01 -1.06307983e+00 -1.22986794e+00 -1.50417137e+00 -1.69520760e+00 -1.78725243e+00 -1.38275337e+00 -6.30519867e-01 -3.87168884e-01 -5.41465759e-01 -5.90938568e-01 -4.63226318e-01 -2.64402390e-01 -3.23934555e-01 -5.30389786e-01 -6.52269363e-01 -6.04104996e-01 -4.41150665e-01 -2.33337402e-01 -1.49421692e-01 -1.98699951e-01 -3.01952362e-02 -4.86125946e-02 1.69277191e-02 4.79564667e-02 -2.50669479e-01 -2.31401443e-01 -3.70431900e-01 -4.35684204e-01 -3.32504272e-01 -1.46720886e-01 2.22520828e-01 1.80967331e-01 5.76183319e-01 8.29935074e-01 7.32452393e-01 6.65700912e-01 8.65821838e-01 1.05656624e+00 1.38643837e+00 1.49241447e+00 1.16077805e+00 8.72938156e-01 5.21093369e-01 -8.93688202e-02 -5.04625320e-01 -1.40525818e-01 5.62347412e-01 5.87287903e-01 3.58514786e-01 6.22714996e-01 6.32682800e-01 6.62668228e-01 4.75217819e-01 3.83752823e-01 3.09999466e-01 2.45405197e-01 1.82857513e-01 3.37989807e-01 6.29901886e-01 7.74497986e-01 7.67925262e-01 7.86268234e-01 6.77295685e-01 7.30808258e-01 5.98949432e-01 2.80160904e-01 2.79836655e-01 2.64696121e-01 5.09326935e-01 3.86175156e-01 3.64435196e-01 4.29010391e-01 7.15179443e-02 2.35881805e-02 -8.67088318e-01 -1.12282562e+00 -1.14631271e+00 -5.93595505e-01 -4.13314819e-01 -1.67369843e-01 1.86334610e-01 2.44777679e-01 1.71716690e-01 4.34734344e-01 6.07044220e-01 7.93394089e-01 1.22477341e+00 1.33336067e+00 8.59451294e-01 8.75740051e-02 -2.89623260e-01 -4.35768127e-01 -4.64048386e-01 -1.93256378e-01 -4.48507309e-01 -8.22219849e-01 -9.36044693e-01 -9.79045868e-01 -1.22677040e+00 -1.30173683e+00 -1.51760292e+00 -1.52563858e+00 -9.77664948e-01 -4.77426529e-01 -3.43378067e-01 -2.53274918e-01 3.01227570e-02 1.88432693e-01 1.54067993e-01 2.32234955e-01 -2.42431641e-01 -5.86479187e-01 -6.26592636e-01 -4.15864944e-01 -4.24762726e-01 -1.29007339e-01 3.35536957e-01 5.43600082e-01 5.77035904e-01 7.28227615e-01 7.63870239e-01 9.35022354e-01 1.43855286e+00 1.80064392e+00 1.61512184e+00 1.34798622e+00 9.90417480e-01 6.35974884e-01 1.44687653e-01 -4.29273605e-01 -8.64080429e-01 -1.06239510e+00 -1.33602142e+00 -1.38485718e+00 -1.45049477e+00 -1.36517525e+00 -1.56078911e+00 -1.08126640e+00 -9.09395218e-01 -4.21377182e-01 -8.37020874e-02 2.02125549e-01 -1.22554779e-01 -4.23446655e-01 -6.64651871e-01 -8.22101593e-01 -1.00556564e+00 -1.04051399e+00 -6.93565369e-01 -5.34931183e-01 -1.46629333e-01 2.02121735e-02 4.74720001e-02 1.55788422e-01 3.21130753e-01 4.23690796e-01 7.65533447e-01 4.70487595e-01 2.64822006e-01 -6.47014618e-01 -9.54801559e-01 -6.95472717e-01 -2.74644852e-01 1.57421112e-01 -8.35151672e-02 -3.44514847e-01 -2.91887283e-01 -3.76529694e-01 -2.50135422e-01 -8.24470520e-02 -1.55830383e-01 -3.41175079e-01 -4.39523697e-01 -6.64346695e-01 -3.01654816e-01 5.54828644e-02 5.02494812e-01 3.12650681e-01 1.41061783e-01 -4.38919067e-02 2.30691910e-01 5.74676514e-01 9.21855927e-01 7.70315170e-01 6.29562378e-01 4.70785141e-01 5.29336929e-01 8.58333588e-01 1.03217316e+00 1.18099022e+00 1.49545479e+00 1.79587364e+00 2.12562943e+00 2.60789680e+00 2.95858192e+00 2.75204086e+00 2.70796967e+00 2.30891037e+00 1.67467499e+00 1.20045280e+00 4.08016205e-01 -1.21847153e-01 -6.37327194e-01 -6.85283661e-01 -5.89282990e-01 -8.67475510e-01]
alist = ['JFM','FMA','MAM','AMJ','MJJ','JJA','JAS','OND','NDJ','DJF']
templist=[]
for i in range(0,68):
for j in range(0,10):
templist.append(alist[j])
templist.append('JFM')
templist.append('FMA')
def moving_average(x,w): # consecutive 3-month running mean SST anomalies exceed the threshold.
return np.convolve(x, np.ones(w),'valid') / w
mean_anoma = moving_average(anoma,3)
df = pd.DataFrame(mean_anoma,columns=['mean_3_anoma'])
df['mo'] = pd.DataFrame(templist)
def get_date_list(begin_date,end_date):
date_list = [x.strftime('%Y-%m-%d') for x in list(pd.date_range(start=begin_date,end=end_date,freq='M'))]
return date_list
date = get_date_list('1960-01-15','2016-12-15')
df['date'] = pd.DataFrame(date)
# Visualize the computed Niño 3.4.
df.plot(x="date",y="mean_3_anoma")
#plt.axhline(y=0.5,ls="-",c="red")
plt.axhline(y=0,ls=":",c="black")
#plt.axhline(y=-0.5,ls="-",c="blue")
<matplotlib.lines.Line2D at 0x1e3305412b0>
d = xr.open_dataset('CERES_EBAF-TOA_200003-201701.nc',engine='netcdf4')
fig = plt.figure(figsize=(10,6), dpi=120)
grid = plt.GridSpec(5, 9)
plt.subplot(grid[0:2, 0:3])
ax1 = d.toa_sw_all_mon.mean(dim='time').plot()
plt.title('TOA longwave flux for all-sky',fontsize=10)
plt.subplot(grid[0:2, 3:6])
ax2 = d.toa_lw_all_mon.mean(dim='time').plot()
plt.title('TOA shortwave flux for all-sky',fontsize=10)
plt.subplot(grid[0:2, 6:9])
ax3 = d.solar_mon.mean(dim='time').plot()
plt.title('TOA solar radiation for all-sky',fontsize=10)
plt.subplot(grid[2:5, 0:5])
ax4 = d.toa_net_all_mon.mean(dim='time').plot()
plt.title('TOA Net Flux All-Sky')
plt.subplot(grid[2:5, 4:10])
ax5 = (d.solar_mon.mean(dim='time') - d.toa_sw_all_mon.mean(dim='time') - d.toa_lw_all_mon.mean(dim='time') ).plot()
plt.title('Add up three variables')
plt.tight_layout()
temp=[]
x = d.solar_mon.mean(dim='time') - d.toa_sw_all_mon.mean(dim='time') - d.toa_lw_all_mon.mean(dim='time')
y = d.toa_net_all_mon.mean(dim='time')
(x-y).sum()
<xarray.DataArray ()> array(1.6990367, dtype=float32)
array(1.6990367, dtype=float32)
weights = np.cos(np.deg2rad(d.lat))
net_weighted = d.toa_net_all_mon.weighted(weights)
region_mean = net_weighted.sum(dim=['lon','time']).plot()
plt.xlabel('latitude/°',fontsize=15)
plt.ylabel('total amount of net radiation/ W',fontsize=15)
Text(0, 0.5, 'total amount of net radiation/ W')
fig = plt.figure(figsize=(10,6), dpi=120)
grid = plt.GridSpec(4, 4)
plt.subplot(grid[0:2, 0:2])
d.toa_sw_all_mon.where((d.cldarea_total_daynight_mon<=25)).mean(dim='time').plot()
plt.title('sw-low cloud area regions')
plt.subplot(grid[0:2, 2:4])
d.toa_sw_all_mon.where((d.cldarea_total_daynight_mon>=75)).mean(dim='time').plot()
plt.title('sw-high cloud area regions')
plt.subplot(grid[2:4, 0:2])
d.toa_lw_all_mon.where((d.cldarea_total_daynight_mon<=25)).mean(dim='time').plot()
plt.title('lw-low cloud area regions')
plt.subplot(grid[2:4, 2:4])
d.toa_lw_all_mon.where((d.cldarea_total_daynight_mon>=75)).mean(dim='time').plot()
plt.title('lw-high cloud area regions')
plt.tight_layout()
fig = plt.figure(figsize=(18,6), dpi=600)
grid = plt.GridSpec(1, 2)
plt.subplot(grid[0, 0])
d.toa_sw_all_mon.weighted(weights).mean(dim=['lon','lat']).plot(label='global')
d.toa_sw_all_mon.where((d.cldarea_total_daynight_mon>=75)).weighted(weights).mean(dim=['lon','lat']).plot(label='high cloud region')
d.toa_sw_all_mon.where((d.cldarea_total_daynight_mon<=25)).weighted(weights).mean(dim=['lon','lat']).plot(label='low cloud region')
plt.legend(loc='best')
plt.xlabel('time',fontsize=18)
plt.ylabel('toa_sw_all_mon',fontsize=18)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.subplot(grid[0, 1])
d.toa_lw_all_mon.weighted(weights).mean(dim=['lon','lat']).plot(label='global')
d.toa_lw_all_mon.where((d.cldarea_total_daynight_mon>=75)).weighted(weights).mean(dim=['lon','lat']).plot(label='high cloud region')
d.toa_lw_all_mon.where((d.cldarea_total_daynight_mon<=25)).weighted(weights).mean(dim=['lon','lat']).plot(label='low cloud region')
plt.legend(loc='best')
plt.xlabel('time',fontsize=18)
plt.ylabel('toa_lw_all_mon',fontsize=18)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.tight_layout()
T = xr.open_dataset('2016-2020-T.nc',engine="netcdf4")
T.t2m.mean(dim=('latitude', 'longitude'))
weight = np.cos(np.deg2rad(T.latitude))
T_weighted = T.t2m.weighted(weight)
region_mean = T_weighted.mean(dim=('longitude','latitude'))
a = region_mean.values
index = pd.date_range('2016-01-01', periods=1827, freq='D')
k = 0
t_day = np.zeros(1827)
for i in range(0,1827):
t_day[i]=a[k:k+24].mean()
k+=24
tempdf = pd.DataFrame(t_day,columns=['t2m'])
tempdf['date'] = index
tempdf['year'] = pd.DatetimeIndex(tempdf.date).year
tempdf['month'] = pd.DatetimeIndex(tempdf.date).month
t_month = tempdf.groupby(['year','month']).mean()
monthly_T = region_mean.groupby('time.month').mean()
sea = np.zeros(60)
for i in range(0,5):
sea[12*i:12*i+12] = monthly_T
certain = t_month['t2m'] - sea
certain.plot()
<AxesSubplot:xlabel='year,month'>
T.t2m.mean(dim='time').plot()
<matplotlib.collections.QuadMesh at 0x1e3308c6ac0>
mu = 100
sigma = 15
x = region_mean.values
num_bins = 50
fig, ax = plt.subplots()
n, bins, patches = ax.hist(x, num_bins, density=1)
plt.title('Histogram of T: $\mu=100$, $\sigma=15$')
plt.xlabel('temperature')
plt.ylabel('probability dendity')
fig.tight_layout()
plt.show()
plt.boxplot(region_mean.values)
{'whiskers': [<matplotlib.lines.Line2D at 0x1e330277790>,
<matplotlib.lines.Line2D at 0x1e3300840a0>],
'caps': [<matplotlib.lines.Line2D at 0x1e330084430>,
<matplotlib.lines.Line2D at 0x1e3300847c0>],
'boxes': [<matplotlib.lines.Line2D at 0x1e3300aea60>],
'medians': [<matplotlib.lines.Line2D at 0x1e330084b50>],
'fliers': [<matplotlib.lines.Line2D at 0x1e330084ee0>],
'means': []}
T_weighted.mean(dim=('longitude','latitude')).plot()
[<matplotlib.lines.Line2D at 0x1e330234190>]
monthly_T.plot()
[<matplotlib.lines.Line2D at 0x1e32cd82580>]